{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Programming Exercise 4: Neural Networks Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import scipy.io #Used to load the OCTAVE *.mat files\n",
    "import scipy.misc #Used to show matrix as an image\n",
    "import matplotlib.cm as cm #Used to display images in a specific colormap\n",
    "import random #To pick random images to display\n",
    "import scipy.optimize #fmin_cg to train neural network\n",
    "import itertools\n",
    "from scipy.special import expit #Vectorized sigmoid function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1 Visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'y' shape: (5000, 1). Unique elements in y: [ 1  2  3  4  5  6  7  8  9 10]\n",
      "'X' shape: (5000, 401). X[0] shape: (401,)\n"
     ]
    }
   ],
   "source": [
    "#Note this is actually a symlink... same data as last exercise,\n",
    "#so there's no reason to add another 7MB to my github repo...\n",
    "datafile = 'data/ex4data1.mat'\n",
    "mat = scipy.io.loadmat( datafile )\n",
    "X, y = mat['X'], mat['y']\n",
    "#Insert a column of 1's to X as usual\n",
    "X = np.insert(X,0,1,axis=1)\n",
    "print \"'y' shape: %s. Unique elements in y: %s\"%(mat['y'].shape,np.unique(mat['y']))\n",
    "print \"'X' shape: %s. X[0] shape: %s\"%(X.shape,X[0].shape)\n",
    "#X is 5000 images. Each image is a row. Each image has 400 pixels unrolled (20x20)\n",
    "#y is a classification for each image. 1-10, where \"10\" is the handwritten \"0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def getDatumImg(row):\n",
    "    \"\"\"\n",
    "    Function that is handed a single np array with shape 1x400,\n",
    "    crates an image object from it, and returns it\n",
    "    \"\"\"\n",
    "    width, height = 20, 20\n",
    "    square = row[1:].reshape(width,height)\n",
    "    return square.T\n",
    "    \n",
    "def displayData(indices_to_display = None):\n",
    "    \"\"\"\n",
    "    Function that picks 100 random rows from X, creates a 20x20 image from each,\n",
    "    then stitches them together into a 10x10 grid of images, and shows it.\n",
    "    \"\"\"\n",
    "    width, height = 20, 20\n",
    "    nrows, ncols = 10, 10\n",
    "    if not indices_to_display:\n",
    "        indices_to_display = random.sample(range(X.shape[0]), nrows*ncols)\n",
    "        \n",
    "    big_picture = np.zeros((height*nrows,width*ncols))\n",
    "    \n",
    "    irow, icol = 0, 0\n",
    "    for idx in indices_to_display:\n",
    "        if icol == ncols:\n",
    "            irow += 1\n",
    "            icol  = 0\n",
    "        iimg = getDatumImg(X[idx])\n",
    "        big_picture[irow*height:irow*height+iimg.shape[0],icol*width:icol*width+iimg.shape[1]] = iimg\n",
    "        icol += 1\n",
    "    fig = plt.figure(figsize=(6,6))\n",
    "    img = scipy.misc.toimage( big_picture )\n",
    "    plt.imshow(img,cmap = cm.Greys_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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0P3v2DP/4xz/O8O+JxUcCsyKRqCNKAMN8Xwp4dHQUv/vd7+BwOJDP5/Hq1Su8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\nvCX4/9OXzW+qmREAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10feff3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "displayData()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2 Model representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#You have been provided with a set of network parameters (Θ(1),Θ(2)) \n",
    "#already trained by us. These are stored in ex4weights.mat\n",
    "datafile = 'data/ex4weights.mat'\n",
    "mat = scipy.io.loadmat( datafile )\n",
    "Theta1, Theta2 = mat['Theta1'], mat['Theta2']\n",
    "# The matrices Theta1 and Theta2 will now be in your workspace\n",
    "# Theta1 has size 25 x 401\n",
    "# Theta2 has size 10 x 26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# These are some global variables I'm suing to ensure the sizes\n",
    "# of various matrices are correct\n",
    "#these are NOT including bias nits\n",
    "input_layer_size = 400\n",
    "hidden_layer_size = 25\n",
    "output_layer_size = 10 \n",
    "n_training_samples = X.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Some utility functions. There are lot of flattening and\n",
    "#reshaping of theta matrices, the input X matrix, etc...\n",
    "#Nicely shaped matrices make the linear algebra easier when developing,\n",
    "#but the minimization routine (fmin_cg) requires that all inputs\n",
    "\n",
    "def flattenParams(thetas_list):\n",
    "    \"\"\"\n",
    "    Hand this function a list of theta matrices, and it will flatten it\n",
    "    into one long (n,1) shaped numpy array\n",
    "    \"\"\"\n",
    "    flattened_list = [ mytheta.flatten() for mytheta in thetas_list ]\n",
    "    combined = list(itertools.chain.from_iterable(flattened_list))\n",
    "    assert len(combined) == (input_layer_size+1)*hidden_layer_size + \\\n",
    "                            (hidden_layer_size+1)*output_layer_size\n",
    "    return np.array(combined).reshape((len(combined),1))\n",
    "\n",
    "def reshapeParams(flattened_array):\n",
    "    theta1 = flattened_array[:(input_layer_size+1)*hidden_layer_size] \\\n",
    "            .reshape((hidden_layer_size,input_layer_size+1))\n",
    "    theta2 = flattened_array[(input_layer_size+1)*hidden_layer_size:] \\\n",
    "            .reshape((output_layer_size,hidden_layer_size+1))\n",
    "    \n",
    "    return [ theta1, theta2 ]\n",
    "\n",
    "def flattenX(myX):\n",
    "    return np.array(myX.flatten()).reshape((n_training_samples*(input_layer_size+1),1))\n",
    "\n",
    "def reshapeX(flattenedX):\n",
    "    return np.array(flattenedX).reshape((n_training_samples,input_layer_size+1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3 Feedforward and cost function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def computeCost(mythetas_flattened,myX_flattened,myy,mylambda=0.):\n",
    "    \"\"\"\n",
    "    This function takes in:\n",
    "        1) a flattened vector of theta parameters (each theta would go from one\n",
    "           NN layer to the next), the thetas include the bias unit.\n",
    "        2) the flattened training set matrix X, which contains the bias unit first column\n",
    "        3) the label vector y, which has one column\n",
    "    It loops over training points (recommended by the professor, as the linear\n",
    "    algebra version is \"quite complicated\") and:\n",
    "        1) constructs a new \"y\" vector, with 10 rows and 1 column, \n",
    "            with one non-zero entry corresponding to that iteration\n",
    "        2) computes the cost given that y- vector and that training point\n",
    "        3) accumulates all of the costs\n",
    "        4) computes a regularization term (after the loop over training points)\n",
    "    \"\"\"\n",
    "    \n",
    "    # First unroll the parameters\n",
    "    mythetas = reshapeParams(mythetas_flattened)\n",
    "    \n",
    "    # Now unroll X\n",
    "    myX = reshapeX(myX_flattened)\n",
    "    \n",
    "    #This is what will accumulate the total cost\n",
    "    total_cost = 0.\n",
    "    \n",
    "    m = n_training_samples\n",
    "\n",
    "    # Loop over the training points (rows in myX, already contain bias unit)\n",
    "    for irow in xrange(m):\n",
    "        myrow = myX[irow]\n",
    "                \n",
    "        # First compute the hypothesis (this is a (10,1) vector\n",
    "        # of the hypothesis for each possible y-value)\n",
    "        # propagateForward returns (zs, activations) for each layer\n",
    "        # so propagateforward[-1][1] means \"activation for -1st (last) layer\"\n",
    "        myhs = propagateForward(myrow,mythetas)[-1][1]\n",
    "\n",
    "        # Construct a 10x1 \"y\" vector with all zeros and only one \"1\" entry\n",
    "        # note here if the hand-written digit is \"0\", then that corresponds\n",
    "        # to a y- vector with 1 in the 10th spot (different from what the\n",
    "        # homework suggests)\n",
    "        tmpy  = np.zeros((10,1))\n",
    "        tmpy[myy[irow]-1] = 1\n",
    "        \n",
    "        # Compute the cost for this point and y-vector\n",
    "        mycost = -tmpy.T.dot(np.log(myhs))-(1-tmpy.T).dot(np.log(1-myhs))\n",
    "     \n",
    "        # Accumulate the total cost\n",
    "        total_cost += mycost\n",
    "  \n",
    "    # Normalize the total_cost, cast as float\n",
    "    total_cost = float(total_cost) / m\n",
    "    \n",
    "    # Compute the regularization term\n",
    "    total_reg = 0.\n",
    "    for mytheta in mythetas:\n",
    "        total_reg += np.sum(mytheta*mytheta) #element-wise multiplication\n",
    "    total_reg *= float(mylambda)/(2*m)\n",
    "        \n",
    "    return total_cost + total_reg\n",
    "       \n",
    "\n",
    "def propagateForward(row,Thetas):\n",
    "    \"\"\"\n",
    "    Function that given a list of Thetas (NOT flattened), propagates the\n",
    "    row of features forwards, assuming the features ALREADY\n",
    "    include the bias unit in the input layer, and the \n",
    "    Thetas also include the bias unit\n",
    "\n",
    "    The output is a vector with element [0] for the hidden layer,\n",
    "    and element [1] for the output layer\n",
    "        -- Each element is a tuple of (zs, as)\n",
    "        -- where \"zs\" and \"as\" have shape (# of units in that layer, 1)\n",
    "    \n",
    "    ***The 'activations' are the same as \"h\", but this works for many layers\n",
    "    (hence a vector of thetas, not just one theta)\n",
    "    Also, \"h\" is vectorized to do all rows at once...\n",
    "    this function takes in one row at a time***\n",
    "    \"\"\"\n",
    "   \n",
    "    features = row\n",
    "    zs_as_per_layer = []\n",
    "    for i in xrange(len(Thetas)):  \n",
    "        Theta = Thetas[i]\n",
    "        #Theta is (25,401), features are (401, 1)\n",
    "        #so \"z\" comes out to be (25, 1)\n",
    "        #this is one \"z\" value for each unit in the hidden layer\n",
    "        #not counting the bias unit\n",
    "        z = Theta.dot(features).reshape((Theta.shape[0],1))\n",
    "        a = expit(z)\n",
    "        zs_as_per_layer.append( (z, a) )\n",
    "        if i == len(Thetas)-1:\n",
    "            return np.array(zs_as_per_layer)\n",
    "        a = np.insert(a,0,1) #Add the bias unit\n",
    "        features = a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.287629165161\n"
     ]
    }
   ],
   "source": [
    "#Once you are done, using the loaded set of parameters Theta1 and Theta2,\n",
    "#you should see that the cost is about 0.287629\n",
    "myThetas = [ Theta1, Theta2 ]\n",
    "\n",
    "#Note I flatten the thetas vector before handing it to the computeCost routine,\n",
    "#as per the input format of the computeCost function.\n",
    "#It does the unrolling/reshaping itself\n",
    "#I also flatten the X vector, similarly\n",
    "print computeCost(flattenParams(myThetas),flattenX(X),y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.4 Regularized cost function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.384487796243\n"
     ]
    }
   ],
   "source": [
    "#Once you are done, using the loaded set of parameters Theta1 and Theta2,\n",
    "#and lambda = 1, you should see that the cost is about 0.383770\n",
    "myThetas = [ Theta1, Theta2 ]\n",
    "print computeCost(flattenParams(myThetas),flattenX(X),y,mylambda=1.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 Backpropagation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### 2.1 Sigmoid gradient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sigmoidGradient(z):\n",
    "    dummy = expit(z)\n",
    "    return dummy*(1-dummy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 Random initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def genRandThetas():\n",
    "    epsilon_init = 0.12\n",
    "    theta1_shape = (hidden_layer_size, input_layer_size+1)\n",
    "    theta2_shape = (output_layer_size, hidden_layer_size+1)\n",
    "    rand_thetas = [ np.random.rand( *theta1_shape ) * 2 * epsilon_init - epsilon_init, \\\n",
    "                    np.random.rand( *theta2_shape ) * 2 * epsilon_init - epsilon_init]\n",
    "    return rand_thetas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3 Backpropagation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def backPropagate(mythetas_flattened,myX_flattened,myy,mylambda=0.):\n",
    "    \n",
    "    # First unroll the parameters\n",
    "    mythetas = reshapeParams(mythetas_flattened)\n",
    "    \n",
    "    # Now unroll X\n",
    "    myX = reshapeX(myX_flattened)\n",
    "\n",
    "    #Note: the Delta matrices should include the bias unit\n",
    "    #The Delta matrices have the same shape as the theta matrices\n",
    "    Delta1 = np.zeros((hidden_layer_size,input_layer_size+1))\n",
    "    Delta2 = np.zeros((output_layer_size,hidden_layer_size+1))\n",
    "\n",
    "    # Loop over the training points (rows in myX, already contain bias unit)\n",
    "    m = n_training_samples\n",
    "    for irow in xrange(m):\n",
    "        myrow = myX[irow]\n",
    "        a1 = myrow.reshape((input_layer_size+1,1))\n",
    "        # propagateForward returns (zs, activations) for each layer excluding the input layer\n",
    "        temp = propagateForward(myrow,mythetas)\n",
    "        z2 = temp[0][0]\n",
    "        a2 = temp[0][1]\n",
    "        z3 = temp[1][0]\n",
    "        a3 = temp[1][1]\n",
    "        tmpy = np.zeros((10,1))\n",
    "        tmpy[myy[irow]-1] = 1\n",
    "        delta3 = a3 - tmpy \n",
    "        delta2 = mythetas[1].T[1:,:].dot(delta3)*sigmoidGradient(z2) #remove 0th element\n",
    "        a2 = np.insert(a2,0,1,axis=0)\n",
    "        Delta1 += delta2.dot(a1.T) #(25,1)x(1,401) = (25,401) (correct)\n",
    "        Delta2 += delta3.dot(a2.T) #(10,1)x(1,25) = (10,25) (should be 10,26)\n",
    "        \n",
    "    D1 = Delta1/float(m)\n",
    "    D2 = Delta2/float(m)\n",
    "    \n",
    "    #Regularization:\n",
    "    D1[:,1:] = D1[:,1:] + (float(mylambda)/m)*mythetas[0][:,1:]\n",
    "    D2[:,1:] = D2[:,1:] + (float(mylambda)/m)*mythetas[1][:,1:]\n",
    "    \n",
    "    return flattenParams([D1, D2]).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Actually compute D matrices for the Thetas provided\n",
    "flattenedD1D2 = backPropagate(flattenParams(myThetas),flattenX(X),y,mylambda=0.)\n",
    "D1, D2 = reshapeParams(flattenedD1D2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4 Gradient checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def checkGradient(mythetas,myDs,myX,myy,mylambda=0.):\n",
    "    myeps = 0.0001\n",
    "    flattened = flattenParams(mythetas)\n",
    "    flattenedDs = flattenParams(myDs)\n",
    "    myX_flattened = flattenX(myX)\n",
    "    n_elems = len(flattened) \n",
    "    #Pick ten random elements, compute numerical gradient, compare to respective D's\n",
    "    for i in xrange(10):\n",
    "        x = int(np.random.rand()*n_elems)\n",
    "        epsvec = np.zeros((n_elems,1))\n",
    "        epsvec[x] = myeps\n",
    "        cost_high = computeCost(flattened + epsvec,myX_flattened,myy,mylambda)\n",
    "        cost_low  = computeCost(flattened - epsvec,myX_flattened,myy,mylambda)\n",
    "        mygrad = (cost_high - cost_low) / float(2*myeps)\n",
    "        print \"Element: %d. Numerical Gradient = %f. BackProp Gradient = %f.\"%(x,mygrad,flattenedDs[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Element: 7396. Numerical Gradient = 0.000046. BackProp Gradient = 0.000046.\n",
      "Element: 5523. Numerical Gradient = 0.000060. BackProp Gradient = 0.000060.\n",
      "Element: 9584. Numerical Gradient = 0.000000. BackProp Gradient = 0.000000.\n",
      "Element: 1020. Numerical Gradient = -0.000173. BackProp Gradient = -0.000173.\n",
      "Element: 5080. Numerical Gradient = 0.000244. BackProp Gradient = 0.000244.\n",
      "Element: 8017. Numerical Gradient = 0.000000. BackProp Gradient = 0.000000.\n",
      "Element: 482. Numerical Gradient = 0.000000. BackProp Gradient = 0.000000.\n",
      "Element: 393. Numerical Gradient = -0.000001. BackProp Gradient = -0.000001.\n",
      "Element: 3981. Numerical Gradient = 0.000143. BackProp Gradient = 0.000143.\n",
      "Element: 9346. Numerical Gradient = 0.000042. BackProp Gradient = 0.000042.\n"
     ]
    }
   ],
   "source": [
    "checkGradient(myThetas,[D1, D2],X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.5 Regularized Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#My back propagation already has regularization in it.\n",
    "#For now, I will assume the regularization part is correct\n",
    "#(since in this case the regularization code is simple, I'm quite confident)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### 2.5 Learning parameters using fmincg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Here I will use scipy.optimize.fmin_cg\n",
    "\n",
    "def trainNN(mylambda=0.):\n",
    "    \"\"\"\n",
    "    Function that generates random initial theta matrices, optimizes them,\n",
    "    and returns a list of two re-shaped theta matrices\n",
    "    \"\"\"\n",
    "\n",
    "    randomThetas_unrolled = flattenParams(genRandThetas())\n",
    "    result = scipy.optimize.fmin_cg(computeCost, x0=randomThetas_unrolled, fprime=backPropagate, \\\n",
    "                               args=(flattenX(X),y,mylambda),maxiter=50,disp=True,full_output=True)\n",
    "    return reshapeParams(result[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Maximum number of iterations has been exceeded.\n",
      "         Current function value: 0.295915\n",
      "         Iterations: 50\n",
      "         Function evaluations: 108\n",
      "         Gradient evaluations: 108\n"
     ]
    }
   ],
   "source": [
    "#Training the NN takes about ~70-80 seconds on my machine\n",
    "learned_Thetas = trainNN()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#If your implementation is correct, you should see a reported training accuracy of about 95.3%\n",
    "#(this may vary by about 1% due to the random initialization)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def predictNN(row,Thetas):\n",
    "    \"\"\"\n",
    "    Function that takes a row of features, propagates them through the\n",
    "    NN, and returns the predicted integer that was hand written\n",
    "    \"\"\"\n",
    "    classes = range(1,10) + [10]\n",
    "    output = propagateForward(row,Thetas)\n",
    "    #-1 means last layer, 1 means \"a\" instead of \"z\"\n",
    "    return classes[np.argmax(output[-1][1])] \n",
    "\n",
    "def computeAccuracy(myX,myThetas,myy):\n",
    "    \"\"\"\n",
    "    Function that loops over all of the rows in X (all of the handwritten images)\n",
    "    and predicts what digit is written given the thetas. Check if it's correct, and\n",
    "    compute an efficiency.\n",
    "    \"\"\"\n",
    "    n_correct, n_total = 0, myX.shape[0]\n",
    "    for irow in xrange(n_total):\n",
    "        if int(predictNN(myX[irow],myThetas)) == int(myy[irow]): \n",
    "            n_correct += 1\n",
    "    print \"Training set accuracy: %0.1f%%\"%(100*(float(n_correct)/n_total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set accuracy: 96.2%\n"
     ]
    }
   ],
   "source": [
    "computeAccuracy(X,learned_Thetas,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Maximum number of iterations has been exceeded.\n",
      "         Current function value: 1.073837\n",
      "         Iterations: 50\n",
      "         Function evaluations: 143\n",
      "         Gradient evaluations: 142\n"
     ]
    }
   ],
   "source": [
    "#Let's see if I set lambda to 10, if I get the same thing\n",
    "learned_regularized_Thetas = trainNN(mylambda=10.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set accuracy: 93.1%\n"
     ]
    }
   ],
   "source": [
    "computeAccuracy(X,learned_regularized_Thetas,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 Visualizing the hidden layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def displayHiddenLayer(myTheta):\n",
    "    \"\"\"\n",
    "    Function that takes slices of the first Theta matrix (that goes from\n",
    "    the input layer to the hidden layer), removes the bias unit, and reshapes\n",
    "    it into a 20x20 image, and shows it\n",
    "    \"\"\"\n",
    "    #remove bias unit:\n",
    "    myTheta = myTheta[:,1:]\n",
    "    assert myTheta.shape == (25,400)\n",
    "    \n",
    "    width, height = 20, 20\n",
    "    nrows, ncols = 5, 5\n",
    "        \n",
    "    big_picture = np.zeros((height*nrows,width*ncols))\n",
    "    \n",
    "    irow, icol = 0, 0\n",
    "    for row in myTheta:\n",
    "        if icol == ncols:\n",
    "            irow += 1\n",
    "            icol  = 0\n",
    "        #add bias unit back in?\n",
    "        iimg = getDatumImg(np.insert(row,0,1))\n",
    "        big_picture[irow*height:irow*height+iimg.shape[0],icol*width:icol*width+iimg.shape[1]] = iimg\n",
    "        icol += 1\n",
    "    fig = plt.figure(figsize=(6,6))\n",
    "    img = scipy.misc.toimage( big_picture )\n",
    "    plt.imshow(img,cmap = cm.Greys_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WOTg369f56nYS5AvDNnNwfS7l0LFrdJElgMwq0ogdpDje5ZJOn0uLUVUP9X52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\n2qD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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1138b4910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "displayHiddenLayer(learned_Thetas[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
